论文标题
基于云的计算模型预测控制使用并行多块ADMM方法
Cloud-based computational model predictive control using a parallel multi-block ADMM approach
论文作者
论文摘要
用于解决每个样本步骤中具有实时需求的大规模系统或系统的非凸问题的重型计算负载已被认为是防止更广泛应用非线性模型预测性控制（NMPC）的原因之一。为了通过输入非线性提高NMPC的实时可行性，我们通过使用精心设计的平行设计的多块交替方向方法（ADMM）算法来设计一种称为云的计算模型预测控制（MPC）的创新方案。这种新颖的平行多块ADMM算法是为解决非线性约束解决非凸问题的计算问题而量身定制的。
Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonlinearity, we devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multi-block alternating direction method of multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints.
